Impact Of Fii On

A RESEARCH PROJECT ON

IMPACT OF FII ON CAPITAL MARKET (An Empirical Study On Indian Capital Markets)

Submitted to: Yamini Karmarkar (HOD MMS-5Yrs, IIPS) Verma sem.

Submitted by: Purnendra IM-98-047 MMS-7th

PREFACE This project, in a way, reveals the dependence of Indian capital markets on the FIIs investment for the period starting from January 1993 to September 2001. I have applied a simple linear model to estimate the effect of FII on the stock index. The data analysis tools used in the research is correlation and regression. I have taken seven indices to study the impact of FII on Indian bourses. Two of these indices are Sensex and Nifty while other five are industry specific index of BSE. These seven indices give the close picture of Indian stock exchanges. The FII s started investing in Indian capital market from September 1992. I have taken average monthly data of FIIs and monthly closing index of all the indices

ACKNOWLEDGEMENT Any accomplishment requires the effort and this work is no different. I am extremely grateful to Ms. Yamini Karmarkar (HOD MMS5Yrs., IIPS), for providing me an opportunity to undertake this research project on the impact of FII on Indian capital market. Her invaluable support, teaching and guidance helped me to complete this project successfully. I express my sincere thanks to all my friends who helped me in completing this project. - Purnendra Verma

INTRODUCTION

This research project studies the relationship between FIIs investment and stock indices. For this purpose I selected India’s two major indices i.e. Sensex and S&P CNX Nifty. These two indices, in a way, represent the picture of India’s stock markets. I also selected the five industry specific indices of BSE i.e. BSE CD, BSE CG, BSE FMCG, BSE HC and BSE IT so as to further observe the effect of FII on particular industry. So this project reveals the impact of FII on the Indian capital market. There may be many other factors on which a stock index may depend i.e. Government policies, budgets, bullion market, inflation, economic and political condition of the country, FDI, Re./Dollar exchange rate etc. But for my study I have selected only one independent variable i.e. FII. This study uses the concept of correlation and regression to study the relationship between FII and stock index. The FII started investing in Indian capital market from September 1992when the Indian economy was opened up in the same year. Their investments include equity only. The sample data of FIIs investments consists of monthly average from January 1993 to September 2001 with 105 observations.

Objective: The objective of my research is to find the relationship between the FIIs investment and stock index. I have also analyzed the impact of FII on specific industrial sector indices. Null Hypothesis (Ho): The various BSE indices and S&P CNX Nifty index does not rises with the increase in FIIs investment. Hypothesis (H): The various BSE indices and S&P CNX Nifty index rises with the increase in FIIs investment.

RESEARCH DESIGN Problem: What is the impact of FIIs investment on the Indian capital market? Hypothesis: The various indices of BSE and NSE Nifty rises with the

increase in FIIs investment. What to observe? For my research purpose I selected six indices of BSE i.e. Sensex, BSE CD, BSE CG, BSE FMCG, BSE HC and BSE IT and one index of NSE i.e. S&P CNX Nifty. The sample data of FIIs investments consists of the monthly average from January 1993 to September 2001 with 105 observations. The sample data of Nifty and Sensex consists of the monthly closing index January 1993 to September 2001 with 105 observations while the past three years data has been taken for other BSE indices with 33 observations in each case. How to observe? The data regarding indices of BSE was taken from the site of BSE and BSE yearbook 2001. I got the data on FIIs investment from Reserve Bank of India’s site. The data of NSE Nifty index was obtained from the site of national stock exchange. Other financial sites, newspapers and magazines helped me in collecting the required data.

How to record observation? I have taken the monthly closing index of all the indices. For FIIs I have recorded monthly average of the net investments made by them in the Indian capital market. Net Investments = Purchases – Sales Model: A simple linear relationship has been shown between two variables using correlation and regression as the data analysis tools. One variable is dependent and the other is independent. I have taken FII as the independent variable while the stock index has been taken as dependent variable. The impact of FII has been separately analyzed with each of the index. So, correlation and regression has been separately run between FII and seven indices taking one index at a time. Inference: If the hypothesis holds good then we can infer that FIIs have significant impact on the Indian capital market. This will help the investors to decide on their investments in stocks and shares. If the hypothesis is rejected, or in other words if the null hypothesis is accepted, then FIIs will have no significant impact on the Indian bourses.

LITERATURE REVIEW Some of the literature surveys done for this project are: 1. Impact of U.S. stock market on Indian stock markets – by Bala Arshanapalli and Mukund S. Kulkarni They examined the nature and extent of linkage between the U.S. and the Indian stock markets. The study uses the theory of co-integration to study interdependence between the BSE, NYSE and NASDAQ. The sample data consisted of daily closing prices for the three indices from January 1991 to December 1998 with 2338 observations. The results were in support of the intuitive hypothesis that the Indian stock market was not interrelated to the US stock markets for the entire sample period. It should be noted that stock markets of many countries became increasingly interdependent with the US stock markets during the same time period. India was late in effecting the liberalization policy and when it implanted these policies it did so in a

careful and slow manner. However, as the effect of economic liberalizations started to take place, the BSE became more integrated with the NASDAQ and the NYSE, particularly after 1998. It must be noted that though BSE stock market is integrated with US stock markets, it does not influence the NASDAQ and NYSE markets. 2. Are the structural changes in MF investing, driving the US stock markets to its current levels – by Michael Mosebach and Mohammad Najand (Old Dominion University) They examined the long run equilibrium relation between the net flow of funds into equity MF and the S&P 500 index. Applying the Engel and Granger correction methodology followed by a state space procedure, we find that the levels of the stock market are influenced by the net flow of funds into equity MFs. Their findings indicate that the US equity market appears to be rationally adjusting to a structural change in the behaviour of the US investing public. 3. On stock return seasonality and conditional heteroskedasticity – by Kenneth Beller (Washington State University) and John R. Nofsinger

(Marquette University) They modeled the seasonal volatility of stock returns using GARCH specifications and size-sorted portfolios. Estimation results indicate that there are volatility differences between months of the year and that these seasonal volatility patterns are conditional on firm size. Additionally, they found that seasonal volatility does not explain seasonal returns when the reward for risk is held constant over the sample period. Specifically, their results indicate that much of the abnormal return in January for small firms cannot be entirely attributed to either higher systematic risk or a higher risk premium in January. 4.Price pressure and the role of substitutional investors in closed-end funds –by Richard W.Sias (Washington State University) A trader-intensified transactions database is employed to investigate: (1) the relation between order-flow imbalance closed-end funds share prices and discounts (2) the role of institutional investors in closed-end funds. Empirical results are consistent with the hypothesis that buyers (sellers) of closed-end funds face upward (downward) sloping supply (demand) curves. The results also demonstrate that ownership statistics fail to accurately

reflect institutional investors’ importance in closed-end funds market. The results failed to provide the evidence that institutional investors offset the position of individual investors or that institutional investors face systematic “noise trader risk”. 5.On the dynamic relation between stock prices and exchange rates - by Richard A.Ajayi and Mbodja Mougou (Wayne State University) In this study they imply recent advances in the time-series analysis to examine the inter-temporal relation between stock indices and exchange rates for a sample of eight advanced economies. An error correction model (ECM) of two variables employed to simultaneously estimate short-run and long-run dynamics of variables. The ECM result revealed significant shortrun and long-run relationship between two financial markets. Specifically, the results show that increase in aggregate stock prices has negative shortrun effect on domestic currency value. In the long-run, however, stock prices have positive effect on domestic currency value. On the other hand currency depreciation has negative short-run and long-run effects on stock market.

RESEARCH METHODOLOGY Models: Regression Analysis: This analysis tool performs linear regression analysis by using the "least squares" method to fit a line through a set of observations. We can analyze how a single dependent variable is affected by the values of one or more independent variables — for example, how an athlete's performance is affected by such factors as age, height, and weight. We can apportion shares in the performance measure to each of these three factors, based on a set of performance data, and then use the results to predict the performance of a new, untested athlete. Correlation: This analysis tool and its formulas measure the relationship between two data sets that are scaled to be independent of the unit of measurement. The population correlation calculation returns the covariance of two data sets divided by the product of their standard deviations. We can

use the Correlation tool to determine whether two ranges of data move together — that is, whether large values of one set are associated with large values of the other (positive correlation), whether small values of one set are associated with large values of the other (negative correlation), or whether values in both sets are unrelated (correlation near zero). Data: The sample data consists of 105 observations for FII, Sensex and S&P CNX Nifty starting from January 1993 to September 2001. The sample for other five indices of BSE consists of 33 observations starting from January 1999 to September 2001. I have taken the monthly closing index of all the indices and monthly average of net investments made by FII. The FIIs started investing in Indian capital market from September 1992. The number of scrips under following index are: BSE Sensex – 30 NSE Nifty – 50 BSE Consumer Durables (CD) – 22 BSE Capital Goods (CG) – 49 BSE Fast Moving Consumer Goods (FMCG) – 44

BSE Health Care (HC) – 48 BSE Information Technology (IT) – 42 FII was taken as independent variable. Stock indices were taken as dependent variable. The data was taken from various financial sites.

FINDINGS

The findings for the data sample after applying correlation and regression: Correlation with FII NSE Nifty 0.307 BSE Sensex -0.017 BSE CD -0.011 BSE HC 0.003 BSE FMCG -0.047 BSE CG -0.017 BSE IT 0.236

Multiple R

R2

0.302 0.017 0.0111 0.0067 0.0511 0.0995 0.2302

0.0915 0.0003 0.0001 0.0000 0.0026 0.0099 0.0523

Standard Error 221.1 319578.2 379.6 301 130.6 233.9 1392.3

Findings for the period starting January 1997 to December1998: Correlation with FII NSE Nifty 0.609 BSE Sensex 0.656

Multiple R

R2

0.609 0.656

0.372 0.430

Standard Error 91.8 327.8

RESULTS 1. Impact of FII on Nifty: The effect of FII on Nifty is positive. But the coefficient of correlation is low so the effect is less. The standard error comes out to be 221.1which is high. This does not mean the relation is false but we can say that the error in linear relation is high. 2. Impact of FII on BSE Sensex: The effect of FII on Sensex is negative. So, FII is inversely related to Sensex. But the co-efficient of correlation is

very low so the effect is very less. The standard error comes out to be 319578.2which is very high. This means that the deviation from the mean value is high. This does not mean the relation is false but we can say that the error in linear relation is high. The value of multiple-R is also very less. We can say that FII did not have any significant impact on Sensex during the period of January 1993 to September 2001. 3. Impact of FII on BSE CD: BSE CD is inversely related to FII for the period of January 1999 to September 2001. But the extent of impact is veryvery low as co-efficient of correlation is -0.011. 4. Impact of FII on BSE HC: FII has no significant relation with BSE HC, as the value of correlation is 0.003. This does not mean that there is no relation at all between them. It shows the absence of linear relation between the two variables but not a lack of relationship altogether. 5. Impact of FII on BSE FMCG: BSE FMCG is inversely related to FII for the period of January 1999 to September 2001. But the value of R is low so the degree of relation is low. Standard error in this case is 130.6which is less compared to other standard errors between FII and other stock indices. 6. Impact of FII on BSE CG: BSE CG is also negatively correlated with FII. In this case again the degree of relation is less.

7. Impact of FII on BSE IT: BSE IT is positively correlated with FII for the period of January 1999 to September 2001.The value of correlation is 0.236.

CONCLUSION According to findings and results, I concluded that FII did not have any significant impact on the Indian capital market. Therefore, the null hypothesis is accepted. BSE IT and Nifty showed some positive correlation but rest of the index showed negative correlation with FII. Also the degree of relation was less in all the case. It shows the absence of linear relation between FII and stock index. This does not mean that there is no relationship between them. One of the reasons for absence of any linear relation can also be due to the sample data. The data was taken on monthly basis. The data on daily basis can give more positive results (may be). Also FII is not the only factor affecting the stock indices. There are other major factors that influence the

bourses in the stock market. I also analyzed that FII had significant impact on the stock index for the period starting from January 1997 to December 1999. This shows that FII did not had any significant impact earlier but later on they played an important role in the stock market. The sample data available for sectoral indices was low with just 33 observations that have also hampered the results.

BIBLIOGRAPHY • • • • • • •

www.bseindia.com www.nseindia.com www.rbi.org.in www.equitymaster.com www.etintelligence.com Journal of Financial Research Journal of Management Research

FMS, Delhi

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Vikalpa IIM, Ahmedabad ICFAI Reader ICFAI, Hyderabad Basic Econometrics by Damodar Gujrati Fundamentals of Mathematical Statistics by S.C.Gupta